Decision Models for the Ranking of Agricultural Water Demand Management Strategies in an Arid Region

2017 ◽  
Vol 66 (5) ◽  
pp. 773-783 ◽  
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Mohammad Hadi Shabestari
1995 ◽  
Vol 20 (4) ◽  
pp. 176-187 ◽  
Author(s):  
Atef Hamdy ◽  
Mahmoud Abu-Zeid ◽  
C. Lacirignola

2019 ◽  
Vol 45 ◽  
pp. 649-656 ◽  
Author(s):  
Cecilia Tortajada ◽  
Francisco González-Gómez ◽  
Asit K. Biswas ◽  
Joost Buurman

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2216 ◽  
Author(s):  
Koutiva ◽  
Makropoulos

Integrated urban water management calls for tools that can analyze and simulate the complete cycle including the physical, technical, and social dimensions. Scientific advances created simulation tools able to simulate the urban water cycle as realistically as possible. However, even these tools cannot effectively simulate the social component and quantify how behaviors are shaped by external stress factors, such as climate and policies. In this work, an agent-based modeling tool, urban water agents' behavior (UWAB) is used to simulate the water demand behavior of households and how it is influenced by water demand management strategies and drought conditions. UWAB was applied in Athens, Greece to explore the effect of different water demand management strategies to the reliability of the Athens hydrosystem. The results illustrate the usability of UWAB to support decision makers in identifying how “strict” water demand management measures are needed and when and for how long to deploy them in order to alleviate potential water supply issues.


2021 ◽  
Vol 14 (1) ◽  
pp. 406
Author(s):  
Gabriella Botelho ◽  
Mariza Mello ◽  
Asher Kiperstok ◽  
Karla Oliveira-Esquerre

This study presents a pilot study in suburban households in Salvador, Brazil, inserted in the context of a framework developed to aid water demand management strategies. The framework aims to understand the barrier of subjectivity while identifying consumption habit patterns in households. Six key sets of components create the framework architecture: (1) characterization of the area based on: context, climate, population/area, population growth rate, and water management challenges; (2) a survey to obtain socio-demographic and physical property data of the sample; (3) smart metering and data processing systems to monitor sample water end use; (4) determining daily consumption patterns; (5) analyzing qualitative data through theoretical consumption models to identify relevant variables for the next step; and (6) construction of representative mathematical models of consumption for each daily practice (this item was not included on pilot). It provides a starting point to understand how water demand management strategies can be supported at the user and decision-making level. As a result, improvements to the interview guides used in the pilot were suggested. Furthermore, customized measures to promote rational water consumption were identified in the study area, and policies could be proposed.


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